Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Machine Learning Syllabus Overview
·
Module 2
Dimensionality Reduction with PCA
Why Reduce Dimensions?
Explain the purpose of dimensionality reduction for visualization and computational efficiency.
Understanding PCA: Maximum Variance Directions
Describe the goal of Principal Component Analysis (PCA) as finding orthogonal directions of maximum variance.
3
PCA with Scikit-learn: Transform and Visualize
Perform PCA on a dataset using scikit-learn and visualize the transformed data.
Selecting Principal Components with Explained Variance Ratio
Use the explained variance ratio to select an appropriate number of principal components.
Previous module
Foundations for Practical Machine Learning
Next module
Clustering with K-Means and GMMs